Hypercholesterolemia (particularly low-density lipoprotein-cholesterol (LDL-C)) is a major, modifiable risk factor for atherosclerotic cardiovascular disease (ASCVD), the primary cause of death in the US. Today, an estimated 41 million people in the US are hypercholesterolemia and 75% of these 41 million people take one of seven statin drugs that are remarkably effective in reducing elevated LDL-C and cardiovascular morbidity. However, nearly 55% of statin-treated patients do not achieve target LDL-C levels during the first year of treatment, resulting in preventable mortality and unnecessary health care costs. The most important barrier to achieving target LDL-C levels is the inability to deliver real-time recommendations for optimized statin treatment synthesized from large, evidence-based datasets. In the absence of such decision support, clinicians must choose statins arbitrarily and titrate doses over a prolonged period, generating preventable costs. Preliminary research in a VA hospital setting indicates that Statin Manager (SM), a patent-pending computerized, electronic health care record (EHR)-based algorithm can predict with high accuracy the probability of achieving target LDL-C levels. Using multivariate logistic regression models based on individual patient characteristics, including concomitant clinical conditions and medications, Statin Manager predicts the probability that target LDL-C levels will be achieved by specific statins at specific doses. SM ensures that the right statin, in the right dosage, is prescribed for each patient at the beginning of the treatment regimen. Further development, extension, and commercialization of the statin management algorithm is envisioned to reduce the high cost, extended time and frequent frustration of experimentation to achieve target LDL-C levels, potentially reduce side effects, improve treatment adherence and ultimately reduce the resultant risk of ASCVD associated with elevated LDL-C. The economic savings associated with improved healthcare for ASCVD outcomes is estimated in the tens or hundreds of millions of dollars annually in the US alone. The first aim of this Phase I study uses a sample of ~201,000 statin-treated patients in a regional VA healthcare network to confirm the precision (reliability) of SM in predicting achievement of LCL-C goal by selecting the most efficacious statin and dose to achieve targeted LDL-C levels. We will also explore extension of the algorithm to include statin-related and emergent adverse events potentially impacting optimal statin and dose selection. The second aim is to determine the internal (predictive) validity of SM using data from all statin- treated patients (~5,000,000) in VA's national Corporate Data Warehouse. We will compare LDL-C levels achieved over a broad range of prescribed statins and doses with those predicted by SM. Upon completion of Phase I, SM will have been further validated in two large retrospective EHR studies, thus positioning SM for a prospective, external validation study in Phase II. Ultimately, SM will be designed to meet the requirements of major integrated healthcare systems for inclusion as an embedded application in their EHR system-wide.